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Soils are unconsolidated materials that are result of weathering and erosion process of rocks. When water content of some soils change, it makes problems to civil activities. These problems include swelling, dispersing and collapse. The change of water content of expansive soils causes to changes their volume. The volume change can damage structures that have built on the soils. In dispersive soils, particles move through soils with water flow. It may be conduits form in the soils. Collapsible soils are settled when saturated under loading. The rapid collapse of soils damages the structures which have built on soil. Problematic soils are formed in especial geological conditions. For example, collapsible soils are often founded in semi-arid area. Field observation and laboratory test can be useful to identify problematic soils. Some properties of soils such as dry density and liquid limit are helpful to estimate collapsibility potential of soils. In this regard, it was done a series laboratory tests to evaluate the collapsibility rate.

Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn't available for all wells, especially old wells and it is very important to estimate this parameter using other well logging. Hence, lots of methods have been developed to estimate these data using other available information of reservoir. In this study, after processing and removing inappropriate petrophysical data, we estimated petrophysical properties affecting shear wave velocity of the reservoir and statistical methods were used to establish relationship between effective petrophysical properties and shear wave velocity. To predict (VS), first we used empirical relationships and then multivariate regression methods and neural networks were used. Multiple regression method is a powerful method that uses correlation between available information and desired parameter. Using this method, we can identify parameters affecting estimation of shear wave velocity. Neural networks can also be trained quickly and present a stable model for predicting shear wave velocity. For this reason, this method is known as "dynamic regression" compared with multiple regression. Neural network used in this study is not like a black box because we have used the results of multiple regression that can easily modify prediction of shear wave velocity through appropriate combination of data. The same information that was intended for multiple regression was used as input in neural networks, and shear wave velocity was obtained using compressional wave velocity and well logging data (neutron, density, gamma and deep resistivity) in carbonate rocks. The results show that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92 and 95 percents of correlation coefficient in multiple regression and neural network method, respectively. Therefore, we propose using these methods to estimate shear wave velocity in wells without this parameter. H. Akhundi et al.304

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